Excitation backprop (EBP) [1]} models network attention as a probabilistic winner-take-all (WTA) process.
EBP calculates the probability of traversing to a given node in the convolutional network, with the probabilities being defined by the positive weights and non-negative activations. The output of EBP is a saliency map that localizes regions in the image that are excitory for a given class.
